The problem of inferring ancestral genetic information in terms of a set of founders of a given population arises in various biological contexts. In optimization terms, this problem can be formulated as a
combinatorial string problem. The main problem of existing techniques, both exact and heuristic, is that their time complexity scales exponentially, which makes them impractical for solving large-scale instances. Basing our work on previous ideas outlined in [1], we developed a randomized
itera...

The problem of inferring ancestral genetic information in terms of a set of founders of a given population arises in various biological contexts. In optimization terms, this problem can be formulated as a
combinatorial string problem. The main problem of existing techniques, both exact and heuristic, is that their time complexity scales exponentially, which makes them impractical for solving large-scale instances. Basing our work on previous ideas outlined in [1], we developed a randomized
iterated greedy algorithm that is able to provide good solutions in a short time span. The experimental evaluation shows that our algorithm is currently the best approximate technique, especially when large problem instances are concerned.